Why text data requires special handling in Data Analysis Python - Challenge Your Understanding

Challenge - 5 Problems

🧠 Conceptual

intermediate

Why is text data different from numeric data?

Text data often needs special handling in data science. Which reason below best explains why text data is different from numeric data?

A Text data is stored in binary format, unlike numeric data which is stored as characters.

B Text data can have many variations and meanings, making it hard to analyze directly like numbers.

C Text data is always shorter than numeric data, so it needs compression.

D Text data never contains missing values, unlike numeric data.

💡 Hint

Think about how words can have different forms and meanings compared to simple numbers.

Text data is complex because words can have multiple meanings, spellings, and forms. This makes it harder to analyze directly compared to numeric data, which is straightforward to compute with.

❓ Predict Output

intermediate

Output of tokenizing text data

What is the output of this Python code that splits a sentence into words?

Data Analysis Python

sentence = "Data science is fun!"
tokens = sentence.split()
print(tokens)

A ['Data science is fun!']

B ['Data', 'science', 'is', 'fun', '!']

C ['Data', 'science', 'is', 'fun']

D ['Data', 'science', 'is', 'fun!']

💡 Hint

Remember that split() splits by spaces and keeps punctuation attached to words.

The split() method splits the string at spaces, so the exclamation mark stays attached to 'fun!'.

❓ data_output

advanced

Result of converting text to lowercase

What is the resulting list after converting all words in this list to lowercase?

Data Analysis Python

words = ['Python', 'Data', 'SCIENCE', 'Fun']
lower_words = [w.lower() for w in words]
print(lower_words)

A ['python', 'data', 'science', 'fun']

B ['pYTHON', 'dATA', 'sCIENCE', 'fUN']

C ['PYTHON', 'DATA', 'SCIENCE', 'FUN']

D ['Python', 'Data', 'SCIENCE', 'Fun']

💡 Hint

Think about what the lower() method does to each string.

The lower() method converts all uppercase letters to lowercase, so all words become lowercase.

🔧 Debug

advanced

Identify the error in text preprocessing code

What error will this code raise when trying to remove punctuation from a text string?

Data Analysis Python

import string
text = "Hello, world!"
clean_text = text.replace(string.punctuation, '')
print(clean_text)

A NameError: name 'string' is not defined

B AttributeError: 'str' object has no attribute 'replace'

C TypeError: replace() argument 1 must be str, not 'string.punctuation'

D No error, output: 'Hello, world!'

💡 Hint

Check what type string.punctuation is and what replace() expects.

The replace() method expects a substring to replace, but string.punctuation is a string of multiple punctuation characters, so it does not match any substring exactly. This causes a TypeError because replace expects a string, but the usage is incorrect.

🚀 Application

expert

Choosing the right method to handle text data for sentiment analysis

You want to prepare customer reviews for sentiment analysis. Which approach below best handles the text data before feeding it into a machine learning model?

A Convert all text to lowercase, remove punctuation, tokenize words, and remove common stopwords.

B Keep the text as is, including uppercase letters and punctuation, to preserve original meaning.

C Replace all words with their length numbers to simplify the data.

D Remove all vowels from the text to reduce size.

💡 Hint

Think about common steps in text cleaning for machine learning.

Lowercasing, removing punctuation, tokenizing, and removing stopwords are standard steps to clean text and reduce noise before analysis.